Construction of a prognostic model and multidimensional analysis of hepatocellular carcinoma based on palmitoylation-related genes.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41618052", "kind": "science-record", "revision": 3, "content_hash": "sha256:c3f5e8a215e0bbd1dc4e2a18d55f19b4176874187d66f0f740fbd5ddc8fd0228", "created_at": "2026-11-01T20:31:43Z", "updated_at": "2026-11-01T22:15:33Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction of a prognostic model and multidimensional analysis of hepatocellular carcinoma based on palmitoylation-related genes.", "doi": "10.1007/s12672-026-04538-8", "pmid": "41618052", "authors": ["Hui Zhang", "Huan Su", "Kaimei Xia", "Haiyan Wang", "Yi Wu", "Yan Xiong", "Ting Deng", "Ping Du"], "journal": "Discover oncology", "year": 2026, "abstract": "PURPOSE: This study aims to construct a prognostic model for hepatocellular carcinoma (HCC) based on palmitoylation-related genes and explore its molecular mechanisms through multi-dimensional analyses.\nMETHODS: The research integrated single-cell transcriptome data (GSE189903) with bulk transcriptome data (TCGA-LIHC, GEO datasets), focusing on palmitoylation-related genes in HCC epithelial cells. The scAB deconvolution algorithm was used to analyze the association between epithelial cell subsets and patient survival, and hdWGCNA was combined to construct a gene co-expression network. Through differential expression analysis, univariate Cox regression, and LASSO penalized regression, 7 key genes (SERPINE1, FMO3, ALDH2, CPS1, SLCO1B1, ACAT1, ACADS) were identified to build a prognostic risk model.\nRESULTS: Validation results showed that the model could effectively distinguish the survival prognosis of high-risk and low-risk patients (AUC values for 1/3/5 years in the TCGA cohort were 0.676, 0.656, and 0.642, respectively; those in the GSE14520 validation set were 0.702, 0.658, and 0.654, respectively), and the risk score was an independent prognostic factor. Further analyses revealed that the risk score was associated with tumor staging, immune cell infiltration (e.g., T cells, monocytes), response to immunotherapy, and drug sensitivity. Functional enrichment analysis indicated that the high-risk group was enriched in cell cycle regulation and oncogenic signaling pathways, while the low-risk group was related to metabolic pathways.\nCONCLUSION: This study is the first to analyze the regulatory network of palmitoylation in HCC epithelial cells by combining single-cell and bulk transcriptomes, providing new molecular targets and methodological references for HCC prognosis evaluation and precision therapy.", "claims": [{"id": "claim-method", "statement": "Univariate Cox regression analysis was used to identify candidate genes associated with overall survival (OS). The least absolute shrinkage and selection operator (LASSO) penalized Cox regression analysis and the “glmnet” package in R were applied to construct a gene signature for predicting OS using hepatocellular carcinoma-related palmitoylation candidate genes, with the candidate genes as independent variables and OS as the response variable [30].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41618052", "locator": "sections[6] \"Prognostic analysis of gene characterization\" paragraphs 1-3", "quote": "Univariate Cox regression analysis was used to identify candidate genes associated with overall survival (OS). The least absolute shrinkage and selection operator (LASSO) penalized Cox regression analysis and the “glmnet” package in R were applied to construct a gene signature for predicting OS using hepatocellular carcinoma-related palmitoylation candidate genes, with the candidate genes as independent variables and OS as the response variable [30]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and/or analysed during the current study are available in the Gene Expression Omnibus (GEO) repository, with the accession numbers GSE189903 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE189903), GSE14520 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE14520) and GSE115018 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE115018). All microarray data comply with the MIAME guidelines. The TCGA-LIHC cohort data (transcriptomic and clinical information) are available in the University of California Santa Cruz (UCSC) Xena browser (https://xenabrowser.net/), with the dataset identifier TCGA-LIHC. The palmitoylation-related genes used in this study were retrieved from the GeneCards database (https://www.genecards.org/) using the keyword ‘Palmitoylation’. The protein expression data used for validation are available in the Human Protein Atlas (HPA) repository (https://www.proteinatlas.org/). All datasets are publicly accessible without restrictions.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41618052", "locator": "sections[35] \"notes\" paragraph 1", "quote": "The datasets generated and/or analysed during the current study are available in the Gene Expression Omnibus (GEO) repository, with the accession numbers GSE189903 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE189903), GSE14520 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE14520) and GSE115018 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE115018). All microarray data comply with the MIAME guidelines. The TCGA-LIHC cohort data (transcriptomic and clinical information) are available in the University of California Santa Cruz (UCSC) Xena browser (https://xenabrowser.net/), with the dataset identifier TCGA-LIHC. The palmitoylation-related genes used in this study were retrieved from the GeneCards database (https://www.genecards.org/) using the keyword ‘Palmitoylation’. The protein expression data used for validation are available in the Human Protein Atlas (HPA) repository (https://www.proteinatlas.org/). All datasets are publicly accessible without restrictions."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Evaluation of the model’s accuracy in predicting outcomes showed that the 1-year AUC was 0.676, 3-year AUC was 0.656, and 5-year AUC was 0.642 (Fig. 6A).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41618052", "locator": "sections[20] \"Predictive efficacy of liver cancer prognostic risk model in training and validation sets\" paragraph 2", "quote": "Evaluation of the model’s accuracy in predicting outcomes showed that the 1-year AUC was 0.676, 3-year AUC was 0.656, and 5-year AUC was 0.642 (Fig. 6A)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Evaluation of the model’s accuracy in predicting outcomes showed that the 1-year AUC was 0.676, 3-year AUC was 0.656, and 5-year AUC was 0.642 (Fig. 6A).", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:41618052"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE115018"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE115018"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41618052", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1007/s12672-026-04538-8", "sha256": "sha256:a147aca7374f19e51a281a39201eeea8afbf7c38cb91bc8f83406ddd2355cef4", "bytes": 64827, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1007/s12672-026-04538-8", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12873020", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE115018", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE115018", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE115018", "version": null, "note": "The datasets generated and/or analysed during the current study are available in the Gene Expression Omnibus (GEO) repository, with the accession numbers GSE189903 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE189903), GSE14520 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE14520) and GSE115018 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE115018). All microarray data comply with the MIAME guidelines. The TCGA-LIHC cohort data (transcriptomic and clinical information) are available in the University of California Santa Cruz (UCSC) Xena browser (https://xenabrowser.net/), with the dataset identifier TCGA-LIHC. The palmitoylation-related genes used in this study were retrieved from the GeneCards database (https://www.genecards.org/) using the keyword ‘Palmitoylation’. The protein expression data used for validation are available in the Human Protein Atlas (HPA) repository (https://www.proteinatlas.org/). All datasets are publicly accessible without restrictions.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets generated and/or analysed during the current study are available in the Gene Expression Omnibus (GEO) repository, with the accession numbers GSE189903 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE189903), GSE14520 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE14520) and GSE115018 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE115018). All microarray data comply with the MIAME guidelines. The TCGA-LIHC cohort data (transcriptomic and clinical information) are available in the University of California Santa Cruz (UCSC) Xena browser (https://xenabrowser.net/), with the dataset identifier TCGA-LIHC. The palmitoylation-related genes used in this study were retrieved from the GeneCards database (https://www.genecards.org/) using the keyword ‘Palmitoylation’. The protein expression data used for validation are available in the Human Protein Atlas (HPA) repository (https://www.proteinatlas.org/). All datasets are publicly accessible without restrictions.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM3163056", "GSM3163057", "GSM3163058", "GSM3163059", "GSM3163060", "GSM3163061", "GSM3163062", "GSM3163063", "GSM3163064", "GSM3163065", "GSM3163066", "GSM3163067", "GSM3163068", "GSM3163069", "GSM3163070", "GSM3163071", "GSM3163072", "GSM3163073", "GSM3163074", "GSM3163075", "GSM3163076", "GSM3163077", "GSM3163078", "GSM3163079"], "sample_count": 24, "group_sizes": null, "group_sizes_basis": null, "platform": "20115", "taxon": "Homo sapiens", "title": "Simultaneous control of non-coding RNA on the G2/M phase of the cell cycle in hepatocellular carcinoma [lncRNA and mRNA]", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE14520", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14520", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE14520", "version": null, "note": "The datasets generated and/or analysed during the current study are available in the Gene Expression Omnibus (GEO) repository, with the accession numbers GSE189903 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE189903), GSE14520 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE14520) and GSE115018 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE115018). All microarray data comply with the MIAME guidelines. The TCGA-LIHC cohort data (transcriptomic and clinical information) are available in the University of California Santa Cruz (UCSC) Xena browser (https://xenabrowser.net/), with the dataset identifier TCGA-LIHC. The palmitoylation-related genes used in this study were retrieved from the GeneCards database (https://www.genecards.org/) using the keyword ‘Palmitoylation’. The protein expression data used for validation are available in the Human Protein Atlas (HPA) repository (https://www.proteinatlas.org/). All datasets are publicly accessible without restrictions.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets generated and/or analysed during the current study are available in the Gene Expression Omnibus (GEO) repository, with the accession numbers GSE189903 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE189903), GSE14520 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE14520) and GSE115018 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE115018). All microarray data comply with the MIAME guidelines. The TCGA-LIHC cohort data (transcriptomic and clinical information) are available in the University of California Santa Cruz (UCSC) Xena browser (https://xenabrowser.net/), with the dataset identifier TCGA-LIHC. The palmitoylation-related genes used in this study were retrieved from the GeneCards database (https://www.genecards.org/) using the keyword ‘Palmitoylation’. The protein expression data used for validation are available in the Human Protein Atlas (HPA) repository (https://www.proteinatlas.org/). All datasets are publicly accessible without restrictions.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM362947", "GSM362948", "GSM362949", "GSM362950", "GSM362951", "GSM362952", "GSM362953", "GSM362954", "GSM362955", "GSM362956", "GSM362957", "GSM362958", "GSM362959", "GSM362960", "GSM362961", "GSM362962", "GSM362963", "GSM362964", "GSM362965", "GSM362966", "GSM362967", "GSM362968", "GSM362969", "GSM362970", "GSM362971", "GSM362972", "GSM362973", "GSM362974", "GSM362975", "GSM362976", "GSM362977", "GSM362978", "GSM362979", "GSM362980", "GSM362981", "GSM362982", "GSM362983", "GSM362984", "GSM362985", "GSM362986", "GSM362987", "GSM362988", "GSM362989", "GSM362990", "GSM362991", "GSM362992", "GSM362993", "GSM362994", "GSM362995", "GSM362996", "GSM362997", "GSM362998", "GSM362999", "GSM363000", "GSM363001", "GSM363002", "GSM363003", "GSM363004", "GSM363005", "GSM363006", "GSM363007", "GSM363008", "GSM363009", "GSM363010", "GSM363011", "GSM363012", 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All microarray data comply with the MIAME guidelines. The TCGA-LIHC cohort data (transcriptomic and clinical information) are available in the University of California Santa Cruz (UCSC) Xena browser (https://xenabrowser.net/), with the dataset identifier TCGA-LIHC. The palmitoylation-related genes used in this study were retrieved from the GeneCards database (https://www.genecards.org/) using the keyword ‘Palmitoylation’. The protein expression data used for validation are available in the Human Protein Atlas (HPA) repository (https://www.proteinatlas.org/). All datasets are publicly accessible without restrictions.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets generated and/or analysed during the current study are available in the Gene Expression Omnibus (GEO) repository, with the accession numbers GSE189903 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE189903), GSE14520 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE14520) and GSE115018 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE115018). All microarray data comply with the MIAME guidelines. The TCGA-LIHC cohort data (transcriptomic and clinical information) are available in the University of California Santa Cruz (UCSC) Xena browser (https://xenabrowser.net/), with the dataset identifier TCGA-LIHC. The palmitoylation-related genes used in this study were retrieved from the GeneCards database (https://www.genecards.org/) using the keyword ‘Palmitoylation’. The protein expression data used for validation are available in the Human Protein Atlas (HPA) repository (https://www.proteinatlas.org/). All datasets are publicly accessible without restrictions.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM5709304", "GSM5709305", "GSM5709306", "GSM5709307", "GSM5709308", "GSM5709309", "GSM5709310", "GSM5709311", "GSM5709312", "GSM5709313", "GSM5709314", "GSM5709315", "GSM5709316", "GSM5709317", "GSM5709318", "GSM5709319", "GSM5709320", "GSM5709321", "GSM5709322", "GSM5709323", "GSM5709324", "GSM5709325", "GSM5709326", "GSM5709327", "GSM5709328", "GSM5709329", "GSM5709330", "GSM5709331", "GSM5709332", "GSM5709333", "GSM5709334", "GSM5709335", "GSM5709336", "GSM5709337"], "sample_count": 34, "group_sizes": null, "group_sizes_basis": null, "platform": "24676", "taxon": "Homo sapiens", "title": "Multiregional single-cell transcriptomic analysis of liver cancer", "resolution_checked_at": "2026-11-01T20:16:08Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE115018 not downloaded", "asset:data:geo:GSE115018 licence not read", "asset:data:geo:GSE14520 not downloaded", "asset:data:geo:GSE14520 licence not read", "asset:data:geo:GSE189903 not downloaded", "asset:data:geo:GSE189903 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Univariate Cox regression analysis was used to identify candidate genes associated with overall survival (OS). 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